The digital age has made available many types of personal devices to the average consumer. These personal devices include digital cameras, video cameras, music players, personal digital assistants (PDA), global positioning systems (GPS), and telephones to name a few. Personal devices generate and consume digital content at an enormous rate with the consumer demanding that these devices have more features. The internal capacities of these devices are growing exponentially alongside with processing capabilities but are still lagging in handling content sizes to such devices as computing devices. This problem is made more complicated by the need to control power consumption with personal devices. Consumers are demanding having the best digital content presented to them at the personal device rather than have to transfer information to another device for further processing. For example, today, consumers have to transfer their digital pictures captured on a camera to a computer or printer to see a finished product. Although some cameras have a preview feature, the quality of the digital photo is nowhere near the quality as that of a digital photo provided to the computer or the printer. Even with all three devices, the camera, the computer, and the printer, the digital content has been reduced to a useable format (such as JPEG, BMP, or TIFF) so that these devices can process the resulting photo.
Manufacturers who produce personal devices have to choose between performing rich aggregation of the digital content on the personal device or on another device such as a computing device. In the former case, quality is sacrificed due to limitations such as capacity and power. In the later case, the manufacturer has a growing need to distribute and maintain device-specific drivers in operating systems on different computing devices. If a personal device can carry an encapsulation of algorithms alongside with capture parameters, whereby the algorithms dynamically operate depending on the device, it is possible to perform aggregation of the digital content on the personal device using a set of the algorithms, but also perform rich aggregation of the same digital content on the computing device using the same or subset of the algorithms.
In a first scenario, a digital camera is used with voice capturing ability and GPS. The digital camera captures a series of frames tagged with voice annotations. A user prefers to share the series of frames in a photo-story file. Creating a photo-story file is computationally intensive. The camera firmware can crudely create a photo-story of mediocre quality and limited duration. However, for best results, a computer provides the best quality for processing the sequence of images and voice tags when the data is transferred to it from the camera. The computer has more processing capability as well as storage capacity.
In a second scenario, a digital camera captures sensor data and places it into visible pixel format resulting into digital photographs. The process of digitizing the incoming signal involves a complicated workflow, starting with capturing raw sensor data followed by analog-to-digital conversion and ending with signal processing. In finished form, the digital camera has digital content in an interchangeable format consumable by other devices such as printers and computers. In other words, a user may view, hear, or read the finished form on these other devices. The user wants to view the finished form on the digital camera with the option of transferring the finished form to the printer or computer for further viewing. Unfortunately, the processing abilities of the digital camera is limited coupled with the current technology stripping away much details of the raw sensor data when converting to a digital signal stored on the digital camera. The same is true for an audio signal. When music is captured on an audio device such as an MP3 player, the analog signal is converted and quantized into a digital format stripping away much of the information in order to enable the MP3 player to handle and play the music. However, much of the high fidelity quality of the signal is lost when the raw sensor data is converted into these useable formats.
A better technique would be to transfer the raw sensor data to another device such as a computer to process the data into a finished form maintaining the high fidelity quality. This activity presents new problems whereby a user may have to locate and install the necessary software on the computer to enable the processing of the data on the computer. Furthermore, the user would have to insure the compatibility of the software with the personal device as well as the operating system of the computer. Ultimately, the user may cease to use the product if the ease of use is diminished or involves too much effort.
In both scenarios, the core dilemma remains as to how to provide as much high fidelity as possible into the personal devices without the need for additional or third-party equipment for further processing, but also provide an option to allow the additional or third-party equipment to process information captured on the personal devices to obtain optimal quality without requiring additional software. Today, the choice is either having non-optimal finished digital content using an easy-to-use personal device or having high quality digital content using a complex device that involves copying to and processing at the complex device coupled with installing additional software that has to be maintained.
A method is needed that can process raw sensor data to an output on the personal device rather than convert the raw sensor data into a digital format, but also transfer the raw sensor data to a processing device for further processing using processing steps obtained from the personal device during the transfer without the need to maintain a lot of software on the processing device.